Low-rank approximation of continuous functions in Sobolev spaces with dominating mixed smoothness

Let Ω i ⊂ R n i , i = 1 , . . . , m , be given domains. In this article, we study the low-rank approximation with respect to L 2 (Ω 1 × · · · × Ω m ) of functions from Sobolev spaces with dominating mixed smoothness. To this end, we first estimate the rank of a bivariate approximation, i.e., the rank of the continuous singular value decomposition. In comparison to the case of functions from Sobolev spaces with isotropic smoothness, compare [13, 14], we obtain improved results due to the additional mixed smoothness. This convergence result is then used to study the tensor train decomposition as a method to construct multivariate low-rank approximations of functions from Sobolev spaces with dominating mixed smoothness. We show that this approach is able to beat the curse of dimension.